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1. to build a good BOW dictionary, you'd need ~10000 clusters,and you only have 160 images (that's a laugh with ml). your masks are also somewhat useless here (too artificial)

2. again, when testing later on real world data, you can't "mask" anything, so the approach with the masks is useless (without previous segmentation)

3. My idea is to segment image I want to classify on several (10-50) -- no, you can't chop it up into 5x5 pixel tiles. rather use a sliding window approach with the minimum size of the expected racket (or even a pyramid scheme, to adapt to size

in the end, -- you tried a lot and even got quite far (cool !!), but i think -- this is the wrong bus.

rather concider retraining a one-shot detector with your images, and annotated bounding boxes. this can even be done with as little data as you have !

1. to build a good BOW dictionary, you'd need ~10000 clusters,and you only have 160 images (that's a laugh with ml). your masks are also somewhat useless here (too artificial)

2. again, when testing later on real world data, you can't "mask" anything, so the approach with the masks is useless (without previous segmentation)

3. My "My idea is to segment image I want to classify on several (10-50) (10-50)" -- no, you can't chop it up into 5x5 pixel tiles. rather use a sliding window approach with the minimum size of the expected racket (or even a pyramid scheme, to adapt to size

in the end, -- you tried a lot and even got quite far (cool !!), but i think -- this is the wrong bus.

rather concider retraining a one-shot detector with your images, and annotated bounding boxes. this can even be done with as little data as you have !

1. to build a good BOW dictionary, you'd need ~10000 clusters,and clusters (build from a multiple number of images), and you only have 160 images (that's a laugh with ml). your masks are also somewhat useless here (too artificial)artificial). VLAD descriptors would need far less clusters, but none of it seems feasible, given the sparsity of your data.

2. again, when testing later on real world data, you can't "mask" anything, so the approach with the masks is useless (without previous segmentation)

3. "My idea is to segment image I want to classify on several (10-50)" -- no, you can't chop it up into 5x5 pixel tiles. rather use a sliding window approach with the minimum size of the expected racket (or even a pyramid scheme, to adapt to size

in the end, -- you tried a lot and even got quite far (cool !!), but i think -- this is the wrong bus.

rather concider consider retraining a one-shot detector with your images, and annotated bounding boxes. this can even be done with as little data as you have !